{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hUJIqHdB7UKL"
      },
      "source": [
        "[![Roboflow Notebooks](https://ik.imagekit.io/roboflow/notebooks/template/bannertest2-2.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672932710194)](https://github.com/roboflow/notebooks)\n",
        "\n",
        "# How to Auto Train a Classification Model with Autodistill\n",
        "\n",
        "Autodistill uses big, slower foundation models to train small, faster supervised models. Using `autodistill`, you can go from unlabeled images to inference on a custom model running at the edge with no human intervention in between.\n",
        "\n",
        "![Autodistill Steps](https://media.roboflow.com/open-source/autodistill/steps.jpg)\n",
        "\n",
        "As foundation models get better and better they will increasingly be able to augment or replace humans in the labeling process. We need tools for steering, utilizing, and comparing these models. Additionally, these foundation models are big, expensive, and often gated behind private APIs. For many production use-cases, we need models that can run cheaply and in realtime at the edge.\n",
        "\n",
        "![Autodistill Connections](https://media.roboflow.com/open-source/autodistill/connections.jpg)\n",
        "\n",
        "## Steps in this Tutorial\n",
        "\n",
        "In this tutorial, we are going to cover:\n",
        "\n",
        "- Before you start\n",
        "- Image dataset preperation\n",
        "- Autolabel dataset\n",
        "- Train target model\n",
        "- Evaluate target model\n",
        "- Upload dataset and model to Roboflow\n",
        "\n",
        "Let's get started!\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7LOzs8sv8Iom"
      },
      "source": [
        "## Install Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUz0LEd08J9M",
        "outputId": "68ec9dd5-b7a7-433c-a050-0971136c042d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/55.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.6/55.6 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.4/57.4 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m527.0/527.0 kB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.2/213.2 kB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m155.3/155.3 kB\u001b[0m \u001b[31m19.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.8/67.8 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install autodistill autodistill-clip autodistill-yolov8 supervision roboflow -q"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "84nPvK-A8Ww5"
      },
      "source": [
        "## Import Dependencies\n",
        "\n",
        "First, let's import the dependencies we will use in our project:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "hIwvEGNm8V_D"
      },
      "outputs": [],
      "source": [
        "from autodistill_clip import CLIP\n",
        "from autodistill.detection import CaptionOntology\n",
        "from autodistill_yolov8 import YOLOv8\n",
        "import supervision as sv\n",
        "import roboflow\n",
        "import cv2\n",
        "import os\n",
        "\n",
        "HOME = os.getcwd()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnZR_ni_7s8O"
      },
      "source": [
        "## Download Dataset\n",
        "\n",
        "First, we need a dataset with which to work. Below, paste in a URL to any dataset on [Roboflow Universe](https://universe.roboflow.com). You will need a [free Roboflow account](https://app.roboflow.com) to download a dataset from Universe. Alternatively, you can upload your own dataset into this Colab notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGIV2sYf8Lr4",
        "outputId": "8e5b8a7e-9e96-4531-bb89-4113d33edf93"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\rvisit https://app.roboflow.com/auth-cli to get your authentication token.\n",
            "Paste the authentication token here: ··········\n",
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Downloading Dataset Version Zip in Damaged-Signs-Multi-label-3 to multiclass: 97% [63045632 / 64929672] bytes"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting Dataset Version Zip to Damaged-Signs-Multi-label-3 in multiclass:: 100%|██████████| 1801/1801 [00:00<00:00, 2152.85it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<roboflow.core.dataset.Dataset at 0x7f58953ef310>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "roboflow.login()\n",
        "\n",
        "roboflow.download_dataset(dataset_url=\"https://universe.roboflow.com/jayke-boghean-2pxtg/damaged-signs-multi-label/dataset/3\", model_format=\"multiclass\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Ge1uAS37uIA"
      },
      "source": [
        "## Choose a Prompt\n",
        "\n",
        "To label images with Autodistill, you need to set an Ontology.\n",
        "\n",
        "An Ontology for classification consists of two parts:\n",
        "\n",
        "1. A prompt that will be sent to the foundation model (in this example, CLIP), and;\n",
        "2. A class name to which the prompt maps. This is the class name that will be saved in your dataset. The prompt and class name can be the same.\n",
        "\n",
        "Below, we define an Ontology for two classes:\n",
        "\n",
        "1. damaged sign\n",
        "2. sign\n",
        "\n",
        "We then run CLIP on an example image in the dataset.\n",
        "\n",
        "Substitute the prompts and image name as appropriate in the code snippet below.\n",
        "\n",
        "Feel free to experiment with the prompt until you get the expected result across different images in your dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "-nE1_VCl8MON",
        "outputId": "9a94fd46-e686-4a7a-afda-3561b80e1696"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|████████████████████████████████████████| 338M/338M [00:02<00:00, 138MiB/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "damaged sign\n"
          ]
        }
      ],
      "source": [
        "base_model = CLIP(ontology=CaptionOntology({\"damaged sign\": \"damaged sign\", \"sign\": \"sign\"}))\n",
        "\n",
        "image = \"./Damaged-Signs-Multi-label-3/train/0aad64567664f31b63fe4a5041fd9b89-graffiti-pictures-science_jpg.rf.9049d7164fbf942e158d503e2c607511.jpg\"\n",
        "\n",
        "pred = base_model.predict(image)\n",
        "\n",
        "sv.plot_image(cv2.imread(image), size=(4, 4))\n",
        "\n",
        "classes = base_model.ontology.classes()\n",
        "print(classes[pred.class_id[0]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fwyeXWgg7wO3"
      },
      "source": [
        "## Label Dataset\n",
        "\n",
        "Once you have a prompt that works well for your dataset, we can start labeling our dataset.\n",
        "\n",
        "First, replace the folder name in the code cell below with a link to your `train` dataset. Then, run the code cell.\n",
        "\n",
        "This will create a folder of labeled images called `dataset` on which we can train our model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F51QC7hK8Mr2"
      },
      "outputs": [],
      "source": [
        "base_model.label(input_folder=\"./Damaged-Signs-Multi-label-3/train\", output_folder=\"./dataset\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DLw0692O7xRW"
      },
      "source": [
        "## Train a Classification Model\n",
        "\n",
        "Now we are ready to train a classification model. For this example, we will train an Ultralytics YOLOv8 classifiaction model. Run the code cell below to train a model using your dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6qV3O657ljI",
        "outputId": "ad5aa1f8-0919-466c-a117-dc5decb51d67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt to yolov8n-cls.pt...\n",
            "100%|██████████| 5.28M/5.28M [00:00<00:00, 72.5MB/s]\n",
            "New https://pypi.org/project/ultralytics/8.0.132 available 😃 Update with 'pip install -U ultralytics'\n",
            "Ultralytics YOLOv8.0.81 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=classify, mode=train, model=yolov8n-cls.pt, data=/content/dataset/, epochs=100, patience=50, batch=16, imgsz=224, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=0, resume=False, amp=True, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/classify/train\n",
            "Overriding model.yaml nc=1000 with nc=2\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1       464  ultralytics.nn.modules.Conv                  [3, 16, 3, 2]                 \n",
            "  1                  -1  1      4672  ultralytics.nn.modules.Conv                  [16, 32, 3, 2]                \n",
            "  2                  -1  1      7360  ultralytics.nn.modules.C2f                   [32, 32, 1, True]             \n",
            "  3                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                \n",
            "  4                  -1  2     49664  ultralytics.nn.modules.C2f                   [64, 64, 2, True]             \n",
            "  5                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               \n",
            "  6                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           \n",
            "  7                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              \n",
            "  8                  -1  1    460288  ultralytics.nn.modules.C2f                   [256, 256, 1, True]           \n",
            "  9                  -1  1    332802  ultralytics.nn.modules.Classify              [256, 2]                      \n",
            "YOLOv8n-cls summary: 99 layers, 1440850 parameters, 1440850 gradients, 3.4 GFLOPs\n",
            "Transferred 156/158 items from pretrained weights\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/classify/train', view at http://localhost:6006/\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to yolov8n.pt...\n",
            "100%|██████████| 6.23M/6.23M [00:00<00:00, 55.0MB/s]\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 26 weight(decay=0.0), 27 weight(decay=0.0005), 27 bias\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mRandomResizedCrop(p=1.0, height=224, width=224, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1), HorizontalFlip(p=0.5), ColorJitter(p=0.5, brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[0, 0]), Normalize(p=1.0, mean=(0.0, 0.0, 0.0), std=(1.0, 1.0, 1.0), max_pixel_value=255.0), ToTensorV2(always_apply=True, p=1.0, transpose_mask=False)\n",
            "Image sizes 224 train, 224 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/classify/train\u001b[0m\n",
            "Starting training for 100 epochs...\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "      1/100     0.755G     0.1554         16        224: 100%|██████████| 72/72 [00:11<00:00,  6.38it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00,  7.13it/s]\n",
            "                   all      0.704          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "      2/100     0.755G     0.1497         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.33it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 23.94it/s]\n",
            "                   all      0.704          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "      3/100     0.755G     0.1442         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.78it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 31.69it/s]\n",
            "                   all      0.704          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "      4/100     0.755G       0.14         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.11it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 31.15it/s]\n",
            "                   all        0.7          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "      5/100     0.755G     0.1444         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.18it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 34.14it/s]\n",
            "                   all      0.704          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "      6/100     0.755G     0.1417         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.13it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 20.89it/s]\n",
            "                   all      0.704          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "      7/100     0.755G      0.136         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.44it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 43.35it/s]\n",
            "                   all       0.64          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "      8/100     0.755G     0.1375         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.31it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 36.97it/s]\n",
            "                   all      0.729          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "      9/100     0.755G     0.1381         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.21it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 28.88it/s]\n",
            "                   all      0.749          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     10/100     0.755G     0.1397         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.89it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 20.29it/s]\n",
            "                   all      0.753          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     11/100     0.755G     0.1397         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.07it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 35.38it/s]\n",
            "                   all      0.713          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     12/100     0.755G     0.1376         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.24it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 31.88it/s]\n",
            "                   all      0.692          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     13/100     0.755G     0.1337         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.19it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 28.88it/s]\n",
            "                   all      0.749          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     14/100     0.755G     0.1343         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.78it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 20.21it/s]\n",
            "                   all      0.757          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     15/100     0.755G     0.1356         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.32it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 42.10it/s]\n",
            "                   all      0.745          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     16/100     0.755G     0.1335         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.18it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 34.22it/s]\n",
            "                   all      0.741          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     17/100     0.755G     0.1342         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.21it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 36.61it/s]\n",
            "                   all      0.733          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     18/100     0.755G     0.1336         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.48it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 22.44it/s]\n",
            "                   all      0.713          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     19/100     0.755G     0.1326         16        224: 100%|██████████| 72/72 [00:07<00:00,  9.05it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 37.53it/s]\n",
            "                   all      0.704          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     20/100     0.755G     0.1342         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.24it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 24.40it/s]\n",
            "                   all      0.717          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     21/100     0.755G     0.1333         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.21it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 28.56it/s]\n",
            "                   all      0.733          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     22/100     0.755G     0.1308         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.68it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 17.81it/s]\n",
            "                   all      0.777          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     23/100     0.755G     0.1332         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.21it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 28.78it/s]\n",
            "                   all      0.729          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     24/100     0.755G     0.1317         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.36it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 23.78it/s]\n",
            "                   all      0.717          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     25/100     0.755G     0.1327         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.32it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 28.85it/s]\n",
            "                   all      0.769          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     26/100     0.755G     0.1311         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.09it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 36.49it/s]\n",
            "                   all      0.761          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     27/100     0.755G      0.136         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.12it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 31.87it/s]\n",
            "                   all      0.704          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     28/100     0.755G     0.1298         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.69it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 21.29it/s]\n",
            "                   all      0.761          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     29/100     0.755G     0.1338         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.24it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 26.41it/s]\n",
            "                   all      0.733          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     30/100     0.755G     0.1332         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.14it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 26.27it/s]\n",
            "                   all      0.745          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     31/100     0.755G     0.1307         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.53it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 25.84it/s]\n",
            "                   all      0.745          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     32/100     0.755G      0.129         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.41it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 28.40it/s]\n",
            "                   all      0.749          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     33/100     0.755G     0.1297         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.04it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 36.22it/s]\n",
            "                   all      0.725          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     34/100     0.755G     0.1324         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.06it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 29.27it/s]\n",
            "                   all      0.741          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     35/100     0.755G     0.1329         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.55it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 24.63it/s]\n",
            "                   all      0.713          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     36/100     0.755G     0.1351         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.98it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 35.33it/s]\n",
            "                   all      0.733          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     37/100     0.755G     0.1292         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.13it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 29.77it/s]\n",
            "                   all      0.761          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     38/100     0.755G      0.129         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.09it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 30.60it/s]\n",
            "                   all      0.745          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     39/100     0.755G     0.1332         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.34it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 18.11it/s]\n",
            "                   all      0.704          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     40/100     0.755G     0.1303         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.43it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 29.80it/s]\n",
            "                   all      0.713          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     41/100     0.755G     0.1307         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.09it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 24.81it/s]\n",
            "                   all      0.749          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     42/100     0.755G     0.1263         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.23it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 25.40it/s]\n",
            "                   all      0.749          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     43/100     0.755G     0.1283         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.19it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 26.66it/s]\n",
            "                   all      0.761          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     44/100     0.755G     0.1293         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.56it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 21.91it/s]\n",
            "                   all      0.757          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     45/100     0.755G     0.1283         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.24it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 40.90it/s]\n",
            "                   all      0.741          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     46/100     0.755G     0.1323         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.21it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 26.99it/s]\n",
            "                   all      0.761          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     47/100     0.755G     0.1276         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.51it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 23.05it/s]\n",
            "                   all      0.789          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     48/100     0.755G     0.1259         16        224: 100%|██████████| 72/72 [00:10<00:00,  6.99it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 19.49it/s]\n",
            "                   all      0.773          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     49/100     0.755G      0.125         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.75it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 33.64it/s]\n",
            "                   all      0.761          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     50/100     0.755G     0.1253         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.20it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 26.39it/s]\n",
            "                   all      0.757          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     51/100     0.755G     0.1255         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.20it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 26.72it/s]\n",
            "                   all      0.737          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     52/100     0.755G     0.1227         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.27it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 20.10it/s]\n",
            "                   all      0.757          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     53/100     0.755G     0.1212         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.76it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 23.58it/s]\n",
            "                   all      0.777          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     54/100     0.755G      0.126         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.10it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 26.62it/s]\n",
            "                   all      0.749          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     55/100     0.755G     0.1265         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.18it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 23.77it/s]\n",
            "                   all      0.749          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     56/100     0.755G     0.1287         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.79it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 28.06it/s]\n",
            "                   all      0.733          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     57/100     0.755G     0.1239         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.08it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 35.52it/s]\n",
            "                   all      0.721          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     58/100     0.755G     0.1239         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.16it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 30.29it/s]\n",
            "                   all      0.749          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     59/100     0.755G     0.1243         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.08it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 26.80it/s]\n",
            "                   all      0.773          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     60/100     0.755G     0.1237         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.36it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 25.85it/s]\n",
            "                   all      0.757          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     61/100     0.755G     0.1239         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.20it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 27.54it/s]\n",
            "                   all      0.741          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     62/100     0.755G     0.1231         16        224: 100%|██████████| 72/72 [00:10<00:00,  6.92it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 31.38it/s]\n",
            "                   all      0.725          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     63/100     0.755G     0.1218         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.19it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 36.15it/s]\n",
            "                   all      0.745          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     64/100     0.755G      0.122         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.17it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 30.14it/s]\n",
            "                   all      0.749          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     65/100     0.755G     0.1251         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.33it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 39.87it/s]\n",
            "                   all      0.769          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     66/100     0.755G     0.1192         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.20it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 34.03it/s]\n",
            "                   all      0.753          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     67/100     0.755G     0.1174         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.26it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 28.01it/s]\n",
            "                   all      0.749          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     68/100     0.755G     0.1194         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.22it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 34.72it/s]\n",
            "                   all      0.757          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     69/100     0.755G     0.1185         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.31it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 26.87it/s]\n",
            "                   all      0.794          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     70/100     0.755G      0.119         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.46it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 23.30it/s]\n",
            "                   all      0.769          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     71/100     0.755G     0.1208         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.10it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 25.92it/s]\n",
            "                   all      0.765          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     72/100     0.755G     0.1183         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.17it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 24.72it/s]\n",
            "                   all      0.777          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     73/100     0.755G     0.1158         16        224: 100%|██████████| 72/72 [00:10<00:00,  6.79it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 24.61it/s]\n",
            "                   all      0.753          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     74/100     0.755G     0.1173         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.06it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 27.77it/s]\n",
            "                   all      0.765          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     75/100     0.755G     0.1185         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.54it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 45.16it/s]\n",
            "                   all      0.753          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     76/100     0.755G     0.1186         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.24it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 25.35it/s]\n",
            "                   all      0.785          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     77/100     0.755G     0.1142         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.55it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 27.93it/s]\n",
            "                   all      0.781          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     78/100     0.755G     0.1137         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.19it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 25.46it/s]\n",
            "                   all      0.757          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     79/100     0.755G     0.1135         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.20it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 24.14it/s]\n",
            "                   all      0.794          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     80/100     0.755G     0.1119         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.06it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 35.75it/s]\n",
            "                   all      0.789          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     81/100     0.755G     0.1118         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.43it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 23.17it/s]\n",
            "                   all      0.785          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     82/100     0.755G     0.1121         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.24it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 33.65it/s]\n",
            "                   all      0.785          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     83/100     0.755G     0.1117         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.10it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 23.75it/s]\n",
            "                   all      0.802          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     84/100     0.755G     0.1142         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.09it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 29.52it/s]\n",
            "                   all      0.765          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     85/100     0.755G     0.1125         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.15it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 27.33it/s]\n",
            "                   all      0.745          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     86/100     0.755G     0.1082         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.27it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 53.29it/s]\n",
            "                   all      0.777          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     87/100     0.755G     0.1115         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.17it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 26.30it/s]\n",
            "                   all      0.806          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     88/100     0.755G     0.1104         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.19it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 28.74it/s]\n",
            "                   all      0.798          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     89/100     0.755G     0.1093         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.13it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 24.13it/s]\n",
            "                   all      0.814          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     90/100     0.755G     0.1118         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.46it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 23.77it/s]\n",
            "                   all      0.773          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     91/100     0.755G     0.1071         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.26it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 23.33it/s]\n",
            "                   all      0.802          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     92/100     0.755G      0.104         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.10it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 34.11it/s]\n",
            "                   all       0.83          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     93/100     0.755G     0.1032         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.23it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 21.57it/s]\n",
            "                   all      0.826          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     94/100     0.755G     0.1055         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.35it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 23.01it/s]\n",
            "                   all      0.842          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     95/100     0.755G     0.1076         16        224: 100%|██████████| 72/72 [00:09<00:00,  7.50it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 19.26it/s]\n",
            "                   all      0.806          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     96/100     0.755G      0.104         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.15it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 25.35it/s]\n",
            "                   all      0.802          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     97/100     0.755G     0.1105         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.12it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 24.35it/s]\n",
            "                   all      0.789          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     98/100     0.755G     0.1008         16        224: 100%|██████████| 72/72 [00:11<00:00,  6.26it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 30.80it/s]\n",
            "                   all      0.794          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "     99/100     0.755G     0.1018         16        224: 100%|██████████| 72/72 [00:08<00:00,  8.26it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 28.25it/s]\n",
            "                   all      0.802          1\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n",
            "    100/100     0.755G     0.0999         16        224: 100%|██████████| 72/72 [00:10<00:00,  7.19it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:00<00:00, 23.28it/s]\n",
            "                   all      0.834          1\n",
            "\n",
            "100 epochs completed in 0.293 hours.\n",
            "Optimizer stripped from runs/classify/train/weights/last.pt, 3.0MB\n",
            "Optimizer stripped from runs/classify/train/weights/best.pt, 3.0MB\n",
            "Results saved to \u001b[1mruns/classify/train\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "from autodistill_yolov8 import YOLOv8\n",
        "target_model = YOLOv8(\"yolov8n-cls.pt\")\n",
        "target_model.train(f\"{HOME}/dataset/\", epochs=100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lDCqVQO69Q1z"
      },
      "source": [
        "## Evaluate the Model\n",
        "\n",
        "How does our model perform? Great question! To understand how our model performs, we can visualize the confusion matrix saved after the YOLOv8 training job completed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6tyniI1A9QJ8",
        "outputId": "e9812b7d-bcbf-41bd-8e09-e3b8da17525b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "image 1/1 /content/dataset/valid/damaged sign/IMG_8901_jpg.rf.954ac1fe0615b637515385d4a0afa138.jpg: 224x224 damaged sign 0.96, sign 0.04, 13.6ms\n",
            "Speed: 0.6ms preprocess, 13.6ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 'damaged sign', 1: 'sign'}\n",
            "tensor([0.9636, 0.0364], device='cuda:0')\n"
          ]
        }
      ],
      "source": [
        "from IPython.display import Image\n",
        "import random\n",
        "\n",
        "Image(filename=f'{HOME}/runs/classify/train/confusion_matrix.png', width=600)\n",
        "\n",
        "random_image = random.choice(os.listdir(f\"{HOME}/dataset/valid/damaged sign\"))\n",
        "\n",
        "results = target_model.predict(os.path.join(f\"{HOME}/dataset/valid/damaged sign\", random_image))\n",
        "\n",
        "print(results[0].names)\n",
        "print(results[0].probs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l2VkqlGH9FMD"
      },
      "source": [
        "## Upload Model to Roboflow\n",
        "\n",
        "Optionally, you can upload your model to Roboflow. To do so:\n",
        "\n",
        "1. Create a new project in Roboflow\n",
        "2. Upload your data\n",
        "3. Create a new dataset version\n",
        "4. Run the code below\n",
        "\n",
        "You will need to specify your [project ID and dataset version](https://docs.roboflow.com/rest-api#how-to-find-your-model-id-and-version) below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bzxEoA7c8_Tx"
      },
      "outputs": [],
      "source": [
        "import roboflow\n",
        "\n",
        "PROJECT_ID = \"\"\n",
        "DATASET_VERSION = 1\n",
        "\n",
        "roboflow.login()\n",
        "\n",
        "rf = roboflow.Roboflow()\n",
        "\n",
        "project = rf.workspace().project(PROJECT_ID)\n",
        "project.version(DATASET_VERSION).deploy(model_type=\"yolov8-cls\", model_path=f\"./runs/classify/train/\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "crh1Zdic7mP8"
      },
      "source": [
        "# 🏆 Congratulations\n",
        "\n",
        "### Learning Resources\n",
        "\n",
        "Roboflow has produced many resources that you may find interesting as you advance your knowledge of computer vision:\n",
        "\n",
        "- [Roboflow Notebooks](https://github.com/roboflow/notebooks): A repository of over 20 notebooks that walk through how to train custom models with a range of model types, from YOLOv7 to SegFormer.\n",
        "- [Roboflow YouTube](https://www.youtube.com/c/Roboflow): Our library of videos featuring deep dives into the latest in computer vision, detailed tutorials that accompany our notebooks, and more.\n",
        "- [Roboflow Discuss](https://discuss.roboflow.com/): Have a question about how to do something on Roboflow? Ask your question on our discussion forum.\n",
        "- [Roboflow Models](https://roboflow.com): Learn about state-of-the-art models and their performance. Find links and tutorials to guide your learning.\n",
        "\n",
        "### Convert data formats\n",
        "\n",
        "Roboflow provides free utilities to convert data between dozens of popular computer vision formats. Check out [Roboflow Formats](https://roboflow.com/formats) to find tutorials on how to convert data between formats in a few clicks.\n",
        "\n",
        "### Connect computer vision to your project logic\n",
        "\n",
        "[Roboflow Templates](https://roboflow.com/templates) is a public gallery of code snippets that you can use to connect computer vision to your project logic. Code snippets range from sending emails after inference to measuring object distance between detections."
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}